docstr: Internal torch model that uses TabTransformer as an embedding.
  This is where we are passing through activations and neurons.
  
  Parameters
  ----------
  num_class (int): Number of classes identified.
  cat_feat_origin_cards (list): List of categorical features
docstr_tok: ["Internal","torch","model","that","uses","TabTransformer","as","an","embedding",".","This","is","where","we","are","passing","through","activations","and","neurons",".","Parameters","--","--","--","--","--","num_class","(","int",")",":","Number","of","classes","identified",".","cat_feat_origin_cards","(","list",")",":","List","of","categorical","features"]
code: def __init__(
      self, num_class, feature_dim, num_output_layers, device, params
  ):
      """
      Internal torch model that uses TabTransformer as an embedding.
      This is where we are passing through activations and neurons.
  
      Parameters
      ----------
      num_class (int): Number of classes identified.
      cat_feat_origin_cards (list): List of categorical features
      """
      super().__init__()
      import torch.nn as nn
  
      from .tab_transformer import TabTransformer
  
      self.embed = TabTransformer(**params)
  
      relu = nn.ReLU()
      in_dim = 2 * feature_dim
      lin = nn.Linear(in_dim, in_dim, bias=True)
      lin_out = nn.Linear(in_dim, num_class, bias=True)
      self.fc = [nn.Sequential(*[relu, lin])] * (
          num_output_layers - 1
      ) + [nn.Sequential(*[relu, lin_out])]
  
      # Each individual layer inside needs to be put into the GPU.
      # Calling "self.model.cuda()" (TabTransformer:get_model()) will not put a python list into GPU.
      if device.type == "cuda":
          for layer in range(num_output_layers):
              self.fc[layer] = self.fc[layer].cuda()
  
file_path: autogluon/tabular/src/autogluon/tabular/models/tab_transformer/tab_model_base.py
imports: import torch.nn
label_desc: Use image node_id 1 to create a new TabNet object from inherited base classes: nn with example: obj = TabNet(num_class, feature_dim, num_output_layers, device, params)